Publications by authors named "Claudia I Gonzalez"
Deep neural networks have demonstrated the capability of solving classification problems using hierarchical models, and fuzzy image preprocessing has proven to be efficient in handling uncertainty found in images. This paper presents the combination of fuzzy image edge-detection and the usage of a convolutional neural network for a computer vision system to classify guitar types according to their body model. The focus of this investigation is to compare the effects of performing image-preprocessing techniques on raw data (non-normalized images) with different fuzzy edge-detection methods, specifically fuzzy Sobel, fuzzy Prewitt, and fuzzy morphological gradient, before feeding the images into a convolutional neural network to perform a classification task.
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- The paper presents a type-2 fuzzy edge detection method that first computes image gradients in four directions using a technique called morphological gradient.
- It then uses the general type-2 fuzzy Sugeno integral to evaluate these gradients, determining their association with edges by applying various fuzzy densities and combining them with meet and join operators.
- Experimental results show that this approach offers more robust edge detection, especially in blurry images, and outperforms existing algorithms according to Pratt's Figure of Merit.